In this video, we will be introduced to the next section of this course, this section is about Victor
auto regression, which is basically the victor version of Arima.
So why do we want to do this?
Well, in the previous sections, consider that we only ever looked at a single time series at a time.
Our main assumption when doing so is that the future of the Time series is predictable, using only
past values of that Time series.
Now, one has to wonder, is this a valid assumption?
Well, as with most real world problems, the answer is it depends.
But you can see how in the real world one variable might have some effect on another.
For example, suppose that the number of visitors to a website has increased.
This might have an effect on the number of Web servers used to serve that website.
Your auto scalar will create more machines in order to handle more traffic and both number of visitors
and number of Web servers over time.
Our Time series.
Another example which is quite controversial is that the minimum wage has a negative effect on the number
of jobs, the minimum wage at each point in time is a time series, as are employment levels.
So given these real world situations, we know intuitively that at times there is maybe better predicted
by other variables other than its own past values.
In fact, it should make you question why did we use a rhema in the first place?
In this section, we'll start by following the same basic outline of progressing from theory to code.
You'll see that the theory for vector models is pretty straightforward and is basically just a vector
version of what we saw before.
Well, then look at how to use these vector models in code, which is similar to Arima, but perhaps
a bit inconsistent after doing this.
Well, then go back to looking at theory.
This part is optional, but I think it's interesting.
You'll see how amah models are really just special cases of the vector models we study here.
We'll learn how to convert models from one form to another.
And this should give you a deeper appreciation of what we've studied and how everything is related.